In this paper we develop a Dynamic Stochastic General Equilibrium (DSGE) model for an open economy, and estimate it on euro area data using Bayesian estimation techniques.

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In this paper we develop a Dynamic Stochastic General Equilibrium (DSGE) model for an open economy, and estimate it on euro area data using Bayesian estimation techniques.

This paper analyzes the forecasting performance of an open economy dynamic stochastic general equilibrium (DSGE) model, estimated with Bayesian methods, for the Euro area during 1994Q1-2002Q4. We compare the DSGE model and a few variants of this model to various reduced-form forecasting models such as vector autoregressions (VARs) and vector error correction models (VECM), estimated both by maximum likelihood and two different Bayesian approaches, and traditional benchmark models, e.g., the random walk. The accuracy of point forecasts, interval forecasts and the predictive distribution as a whole are assessed in an out-of-sample rolling event evaluation using several univariate and multivariate measures. The results show that the open economy DSGE model compares well with more empirical models and thus that the tension between rigor and fit in older generations of DSGE models is no longer present. We also critically examine the role of Bayesian model probabilities and other frequently used low-dimensional summaries, e.g., the log determinant statistic, as measures of overall forecasting performance.Bayesian inference, Forecasting, Open economy DSGE model, Vector autoregressive models,

There are many indications that formal methods are not used to their full potential by central banks today. In this paper, using data from Sweden, we demonstrate how BVAR and DSGE models can be used to shed light on questions that policymakers deal with in practice. We compare the forecast performance of BVAR and DSGE models with the Riksbank's official, more subjective forecasts, both in terms of actual forecasts and root mean-squared errors. We also discuss how to combine model-and judgment-based forecasts, and show that the combined forecast performs well out of sample. In addition, we show the advantages of structural analysis and use the models for interpreting the recent development of the inflation rate through historical decompositions. Last, we discuss the monetary transmission mechanism in the models by comparing impulse-response functions.